{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ICEBERG Fragmentation Analysis (on NIST'20 test dataset)\n",
    "\n",
    "This notebook provides scripts to plot the analyzed fragmentation data as 3D curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast\n",
    "from collections import defaultdict\n",
    "from typing import Dict, List, Tuple\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "import seaborn as sns\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from rdkit import Chem\n",
    "import os\n",
    "import matplotlib\n",
    "from ms_pred.common import mass_from_smi\n",
    "from mpl_toolkits.mplot3d.art3d import Poly3DCollection\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from ms_pred.common.plot_utils import set_style\n",
    "\n",
    "set_style()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/mnt/home/runzhong/ms-pred\n"
     ]
    }
   ],
   "source": [
    "%cd ~/ms-pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data\n",
    "Data from ICEBERG inference (obtained when running ``run_scripts/iceberg/07_qualitative_analysis.py``).\n",
    "\n",
    "Please replace `SAVE_PATH` with your custom data from inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "SAVE_PATH = \"results/dag_inten_nist20/split_1_rnd1/qualitative\" # NIST'20 test dataset\n",
    "\n",
    "# LIST OF FILE NAMES is all .csv files in the directory SAVE_PATH\n",
    "LIST_OF_FILE_NAMES = [f for f in os.listdir(SAVE_PATH) if f.endswith('.csv')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(save_path: str, file_name: str) -> pd.DataFrame:\n",
    "    return pd.read_csv(f\"{save_path}/{file_name}\")\n",
    "\n",
    "def load_data_accumulative(save_path: str, file_names: list) -> pd.DataFrame:\n",
    "    df = pd.DataFrame()\n",
    "\n",
    "    not_found_files = 0\n",
    "\n",
    "    for file_name in tqdm(file_names, desc=\"Loading files\", unit=\"file\"):\n",
    "        try:\n",
    "            df = pd.concat([df, load_data(save_path, file_name)])\n",
    "        except FileNotFoundError:\n",
    "            print(f\"File {file_name} not found\")\n",
    "            not_found_files += 1\n",
    "    print (f\"Number of not found files: {not_found_files}\")\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading files: 100%|██████████████████████████████████████████████████████████████| 2161/2161 [01:27<00:00, 24.61file/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of not found files: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Load data -- might take up to an hour to load all files!\n",
    "data = load_data_accumulative(SAVE_PATH, LIST_OF_FILE_NAMES)\n",
    "collision_energies = sorted(data['collision_energy'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "     collision_energy                   smiles  \\\n",
       "0                   5           COc1ccc(N)cc1O   \n",
       "1                   5           COc1ccc(N)cc1O   \n",
       "2                   5           COc1ccc(N)cc1O   \n",
       "3                   5           COc1ccc(N)cc1O   \n",
       "4                   5           COc1ccc(N)cc1O   \n",
       "..                ...                      ...   \n",
       "595               100  Cc1cccc(C)c1NS(C)(=O)=O   \n",
       "596               100  Cc1cccc(C)c1NS(C)(=O)=O   \n",
       "597               100  Cc1cccc(C)c1NS(C)(=O)=O   \n",
       "598               100  Cc1cccc(C)c1NS(C)(=O)=O   \n",
       "599               100  Cc1cccc(C)c1NS(C)(=O)=O   \n",
       "\n",
       "                                                smarts          mz  intensity  \\\n",
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       "..                                                 ...         ...        ...   \n",
       "595  [#6H3]-[#6:6]1:[#6H:4]:[#6H:3]:[#6H:5]:[#6:7](...  119.072950   0.137750   \n",
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       "\n",
       "                 bond_indices  \\\n",
       "0    [(0, 1), (5, 7), (8, 2)]   \n",
       "1    [(0, 1), (5, 7), (8, 2)]   \n",
       "2    [(5, 6), (5, 7), (8, 2)]   \n",
       "3    [(3, 4), (5, 7), (8, 9)]   \n",
       "4    [(1, 2), (4, 5), (5, 7)]   \n",
       "..                        ...   \n",
       "595                  [(8, 9)]   \n",
       "596                  [(8, 9)]   \n",
       "597                  [(8, 9)]   \n",
       "598                  [(8, 9)]   \n",
       "599                  [(8, 9)]   \n",
       "\n",
       "                                       fragment_smarts  \\\n",
       "0         [#6:1]1:[#6:2]:[#6:6]:[#6:5]:[#6:3]:[#6:4]:1   \n",
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       "4            [#6:2].[#6:3]:[#6:4]-[#7:7].[#8:8].[#8:9]   \n",
       "..                                                 ...   \n",
       "595  [#6]-[#6:6]1:[#6:4]:[#6:3]:[#6:5]:[#6:7](-[#6:...   \n",
       "596  [#6]-[#6:6]1:[#6:4]:[#6:3]:[#6:5]:[#6:7](-[#6:...   \n",
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       "598  [#6]-[#6:6]1:[#6:4]:[#6:3]:[#6:5]:[#6:7](-[#6:...   \n",
       "599  [#6]-[#6:6]1:[#6:4]:[#6:3]:[#6:5]:[#6:7](-[#6:...   \n",
       "\n",
       "                     complement_smarts  \n",
       "0            [#6]-[#8:9].[#7:7].[#8:8]  \n",
       "1            [#6]-[#8:9].[#7:7].[#8:8]  \n",
       "2          [#6:6]-[#8:9].[#7:7].[#8:8]  \n",
       "3          [#6:4].[#6:5]:[#6:6]-[#8:9]  \n",
       "4            [#6].[#6:1].[#6:5]:[#6:6]  \n",
       "..                                 ...  \n",
       "595  [#7:9]-[#16:12](=[#8:10])=[#8:11]  \n",
       "596  [#7:9]-[#16:12](=[#8:10])=[#8:11]  \n",
       "597  [#7:9]-[#16:12](=[#8:10])=[#8:11]  \n",
       "598  [#7:9]-[#16:12](=[#8:10])=[#8:11]  \n",
       "599  [#7:9]-[#16:12](=[#8:10])=[#8:11]  \n",
       "\n",
       "[1296600 rows x 8 columns]"
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     "execution_count": 6,
     "metadata": {},
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   "execution_count": 7,
   "metadata": {},
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       "      <td>10</td>\n",
       "      <td>COc1ccc(N)cc1O</td>\n",
       "      <td>[#6H3]-[#8:9]-[#6:6]1:[#6H:2]:[#6H:1]:[#6:4](:...</td>\n",
       "      <td>140.070600</td>\n",
       "      <td>0.639343</td>\n",
       "      <td>[]</td>\n",
       "      <td>[#6]-[#8:9]-[#6:6]1:[#6:2]:[#6:1]:[#6:4](:[#6:...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>20</td>\n",
       "      <td>COc1ccc(N)cc1O</td>\n",
       "      <td>[#6H3]-[#8:9]-[#6:6]1:[#6H:2]:[#6H:1]:[#6:4](:...</td>\n",
       "      <td>139.062770</td>\n",
       "      <td>0.008897</td>\n",
       "      <td>[]</td>\n",
       "      <td>[#6]-[#8:9]-[#6:6]1:[#6:2]:[#6:1]:[#6:4](:[#6:...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>10</td>\n",
       "      <td>CCN1C(=O)C=CC1=O</td>\n",
       "      <td>[#6H3]-[#6H2:1]-[#7:6]1-[#6:4](-[#6H:2]=[#6H:3...</td>\n",
       "      <td>125.047130</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>[]</td>\n",
       "      <td>[#6]-[#6:1]-[#7:6]1-[#6:4](-[#6:2]=[#6:3]-[#6:...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>10</td>\n",
       "      <td>CCN1C(=O)C=CC1=O</td>\n",
       "      <td>[#6H3]-[#6H2:1]-[#7:6]1-[#6:4](-[#6H:2]=[#6H:3...</td>\n",
       "      <td>126.054955</td>\n",
       "      <td>0.434402</td>\n",
       "      <td>[]</td>\n",
       "      <td>[#6]-[#6:1]-[#7:6]1-[#6:4](-[#6:2]=[#6:3]-[#6:...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>20</td>\n",
       "      <td>CCN1C(=O)C=CC1=O</td>\n",
       "      <td>[#6H3]-[#6H2:1]-[#7:6]1-[#6:4](-[#6H:2]=[#6H:3...</td>\n",
       "      <td>125.047130</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>[]</td>\n",
       "      <td>[#6]-[#6:1]-[#7:6]1-[#6:4](-[#6:2]=[#6:3]-[#6:...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>20</td>\n",
       "      <td>CCN1C(=O)C=CC1=O</td>\n",
       "      <td>[#6H3]-[#6H2:1]-[#7:6]1-[#6:4](-[#6H:2]=[#6H:3...</td>\n",
       "      <td>126.054955</td>\n",
       "      <td>0.112564</td>\n",
       "      <td>[]</td>\n",
       "      <td>[#6]-[#6:1]-[#7:6]1-[#6:4](-[#6:2]=[#6:3]-[#6:...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>40</td>\n",
       "      <td>CCN1C(=O)C=CC1=O</td>\n",
       "      <td>[#6H3]-[#6H2:1]-[#7:6]1-[#6:4](-[#6H:2]=[#6H:3...</td>\n",
       "      <td>126.054955</td>\n",
       "      <td>0.000390</td>\n",
       "      <td>[]</td>\n",
       "      <td>[#6]-[#6:1]-[#7:6]1-[#6:4](-[#6:2]=[#6:3]-[#6:...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1515 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     collision_energy            smiles  \\\n",
       "98                  5    COc1ccc(N)cc1O   \n",
       "99                  5    COc1ccc(N)cc1O   \n",
       "198                10    COc1ccc(N)cc1O   \n",
       "199                10    COc1ccc(N)cc1O   \n",
       "298                20    COc1ccc(N)cc1O   \n",
       "..                ...               ...   \n",
       "198                10  CCN1C(=O)C=CC1=O   \n",
       "199                10  CCN1C(=O)C=CC1=O   \n",
       "298                20  CCN1C(=O)C=CC1=O   \n",
       "299                20  CCN1C(=O)C=CC1=O   \n",
       "399                40  CCN1C(=O)C=CC1=O   \n",
       "\n",
       "                                                smarts          mz  intensity  \\\n",
       "98   [#6H3]-[#8:9]-[#6:6]1:[#6H:2]:[#6H:1]:[#6:4](:...  139.062770   0.005603   \n",
       "99   [#6H3]-[#8:9]-[#6:6]1:[#6H:2]:[#6H:1]:[#6:4](:...  140.070600   0.953258   \n",
       "198  [#6H3]-[#8:9]-[#6:6]1:[#6H:2]:[#6H:1]:[#6:4](:...  139.062770   0.009606   \n",
       "199  [#6H3]-[#8:9]-[#6:6]1:[#6H:2]:[#6H:1]:[#6:4](:...  140.070600   0.639343   \n",
       "298  [#6H3]-[#8:9]-[#6:6]1:[#6H:2]:[#6H:1]:[#6:4](:...  139.062770   0.008897   \n",
       "..                                                 ...         ...        ...   \n",
       "198  [#6H3]-[#6H2:1]-[#7:6]1-[#6:4](-[#6H:2]=[#6H:3...  125.047130   0.000140   \n",
       "199  [#6H3]-[#6H2:1]-[#7:6]1-[#6:4](-[#6H:2]=[#6H:3...  126.054955   0.434402   \n",
       "298  [#6H3]-[#6H2:1]-[#7:6]1-[#6:4](-[#6H:2]=[#6H:3...  125.047130   0.000047   \n",
       "299  [#6H3]-[#6H2:1]-[#7:6]1-[#6:4](-[#6H:2]=[#6H:3...  126.054955   0.112564   \n",
       "399  [#6H3]-[#6H2:1]-[#7:6]1-[#6:4](-[#6H:2]=[#6H:3...  126.054955   0.000390   \n",
       "\n",
       "    bond_indices                                    fragment_smarts  \\\n",
       "98            []  [#6]-[#8:9]-[#6:6]1:[#6:2]:[#6:1]:[#6:4](:[#6:...   \n",
       "99            []  [#6]-[#8:9]-[#6:6]1:[#6:2]:[#6:1]:[#6:4](:[#6:...   \n",
       "198           []  [#6]-[#8:9]-[#6:6]1:[#6:2]:[#6:1]:[#6:4](:[#6:...   \n",
       "199           []  [#6]-[#8:9]-[#6:6]1:[#6:2]:[#6:1]:[#6:4](:[#6:...   \n",
       "298           []  [#6]-[#8:9]-[#6:6]1:[#6:2]:[#6:1]:[#6:4](:[#6:...   \n",
       "..           ...                                                ...   \n",
       "198           []  [#6]-[#6:1]-[#7:6]1-[#6:4](-[#6:2]=[#6:3]-[#6:...   \n",
       "199           []  [#6]-[#6:1]-[#7:6]1-[#6:4](-[#6:2]=[#6:3]-[#6:...   \n",
       "298           []  [#6]-[#6:1]-[#7:6]1-[#6:4](-[#6:2]=[#6:3]-[#6:...   \n",
       "299           []  [#6]-[#6:1]-[#7:6]1-[#6:4](-[#6:2]=[#6:3]-[#6:...   \n",
       "399           []  [#6]-[#6:1]-[#7:6]1-[#6:4](-[#6:2]=[#6:3]-[#6:...   \n",
       "\n",
       "    complement_smarts  \n",
       "98                NaN  \n",
       "99                NaN  \n",
       "198               NaN  \n",
       "199               NaN  \n",
       "298               NaN  \n",
       "..                ...  \n",
       "198               NaN  \n",
       "199               NaN  \n",
       "298               NaN  \n",
       "299               NaN  \n",
       "399               NaN  \n",
       "\n",
       "[1515 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter out rows where row['smiles'] has more than 10 heavay atoms\n",
    "data = data[data['smiles'].apply(lambda x: len(Chem.MolFromSmiles(x).GetAtoms()) <= 10)]\n",
    "# show rows where any entry is NaN\n",
    "data[data.isna().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3856309/1240068073.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['mass_from_smi'] = data['smiles'].apply(lambda x: mass_from_smi(x))\n",
      "/tmp/ipykernel_3856309/1240068073.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['abs_diff'] = abs(data['mz'] - data['mass_from_smi'])\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>collision_energy</th>\n",
       "      <th>smiles</th>\n",
       "      <th>smarts</th>\n",
       "      <th>mz</th>\n",
       "      <th>intensity</th>\n",
       "      <th>bond_indices</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>COc1ccc(N)cc1O</td>\n",
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       "      <td>139.063329</td>\n",
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       "      <th>4</th>\n",
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       "      <td>COc1ccc(N)cc1O</td>\n",
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       "      <td>0.000077</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>100</td>\n",
       "      <td>CCN1C(=O)C=CC1=O</td>\n",
       "      <td>[#6H3]-[#6H2:1]-[#7:6]1-[#6:4](-[#6H:2]=[#6H:3...</td>\n",
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       "      <td>[#6:1].[#6:2](=[#6:3]-[#6:5]=[#8:8])-[#6:4]=[#...</td>\n",
       "      <td>[#6].[#7:6]</td>\n",
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       "      <td>27.024028</td>\n",
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       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>100</td>\n",
       "      <td>CCN1C(=O)C=CC1=O</td>\n",
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       "      <td>27.024028</td>\n",
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       "      <td>100</td>\n",
       "      <td>CCN1C(=O)C=CC1=O</td>\n",
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       "      <td>26.987638</td>\n",
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       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>100</td>\n",
       "      <td>CCN1C(=O)C=CC1=O</td>\n",
       "      <td>[#6H3]-[#6H2:1]-[#7:6]1-[#6:4](-[#6H:2]=[#6H:3...</td>\n",
       "      <td>98.060040</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>[(2, 3), (3, 5)]</td>\n",
       "      <td>[#6]-[#6:1]-[#7:6]-[#6:5]=[#8:8].[#6:2].[#8:7]</td>\n",
       "      <td>[#6:3].[#6:4]</td>\n",
       "      <td>125.047678</td>\n",
       "      <td>26.987638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>100</td>\n",
       "      <td>CCN1C(=O)C=CC1=O</td>\n",
       "      <td>[#6H3]-[#6H2:1]-[#7:6]1-[#6:4](-[#6H:2]=[#6H:3...</td>\n",
       "      <td>98.060040</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>[(3, 4), (3, 5), (5, 6)]</td>\n",
       "      <td>[#6]-[#6:1]-[#7:6].[#6:2]=[#6:3].[#8:7].[#8:8]</td>\n",
       "      <td>[#6:4].[#6:5]</td>\n",
       "      <td>125.047678</td>\n",
       "      <td>26.987638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105301 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     collision_energy            smiles  \\\n",
       "0                   5    COc1ccc(N)cc1O   \n",
       "1                   5    COc1ccc(N)cc1O   \n",
       "2                   5    COc1ccc(N)cc1O   \n",
       "3                   5    COc1ccc(N)cc1O   \n",
       "4                   5    COc1ccc(N)cc1O   \n",
       "..                ...               ...   \n",
       "595               100  CCN1C(=O)C=CC1=O   \n",
       "596               100  CCN1C(=O)C=CC1=O   \n",
       "597               100  CCN1C(=O)C=CC1=O   \n",
       "598               100  CCN1C(=O)C=CC1=O   \n",
       "599               100  CCN1C(=O)C=CC1=O   \n",
       "\n",
       "                                                smarts         mz  intensity  \\\n",
       "0    [#6H3]-[#8:9]-[#6:6]1:[#6H:2]:[#6H:1]:[#6:4](:...  80.013090   0.000046   \n",
       "1    [#6H3]-[#8:9]-[#6:6]1:[#6H:2]:[#6H:1]:[#6:4](:...  80.013090   0.000008   \n",
       "2    [#6H3]-[#8:9]-[#6:6]1:[#6H:2]:[#6H:1]:[#6:4](:...  80.025665   0.000196   \n",
       "3    [#6H3]-[#8:9]-[#6:6]1:[#6H:2]:[#6H:1]:[#6:4](:...  80.025665   0.000158   \n",
       "4    [#6H3]-[#8:9]-[#6:6]1:[#6H:2]:[#6H:1]:[#6:4](:...  80.025665   0.000077   \n",
       "..                                                 ...        ...        ...   \n",
       "595  [#6H3]-[#6H2:1]-[#7:6]1-[#6:4](-[#6H:2]=[#6H:3...  98.023650   0.000001   \n",
       "596  [#6H3]-[#6H2:1]-[#7:6]1-[#6:4](-[#6H:2]=[#6H:3...  98.023650   0.000001   \n",
       "597  [#6H3]-[#6H2:1]-[#7:6]1-[#6:4](-[#6H:2]=[#6H:3...  98.060040   0.000002   \n",
       "598  [#6H3]-[#6H2:1]-[#7:6]1-[#6:4](-[#6H:2]=[#6H:3...  98.060040   0.000002   \n",
       "599  [#6H3]-[#6H2:1]-[#7:6]1-[#6:4](-[#6H:2]=[#6H:3...  98.060040   0.000002   \n",
       "\n",
       "                 bond_indices  \\\n",
       "0    [(0, 1), (5, 7), (8, 2)]   \n",
       "1    [(0, 1), (5, 7), (8, 2)]   \n",
       "2    [(5, 6), (5, 7), (8, 2)]   \n",
       "3    [(3, 4), (5, 7), (8, 9)]   \n",
       "4    [(1, 2), (4, 5), (5, 7)]   \n",
       "..                        ...   \n",
       "595  [(0, 1), (5, 6), (6, 7)]   \n",
       "596  [(0, 1), (5, 6), (6, 7)]   \n",
       "597  [(3, 4), (5, 6), (6, 7)]   \n",
       "598          [(2, 3), (3, 5)]   \n",
       "599  [(3, 4), (3, 5), (5, 6)]   \n",
       "\n",
       "                                       fragment_smarts  \\\n",
       "0         [#6:1]1:[#6:2]:[#6:6]:[#6:5]:[#6:3]:[#6:4]:1   \n",
       "1         [#6:1]1:[#6:2]:[#6:6]:[#6:5]:[#6:3]:[#6:4]:1   \n",
       "2            [#6].[#6:1](:[#6:2]):[#6:4]:[#6:3]:[#6:5]   \n",
       "3              [#6].[#6:1]:[#6:2].[#6:3].[#7:7].[#8:8]   \n",
       "4            [#6:2].[#6:3]:[#6:4]-[#7:7].[#8:8].[#8:9]   \n",
       "..                                                 ...   \n",
       "595  [#6:1].[#6:2](=[#6:3]-[#6:5]=[#8:8])-[#6:4]=[#...   \n",
       "596  [#6:1].[#6:2](=[#6:3]-[#6:5]=[#8:8])-[#6:4]=[#...   \n",
       "597     [#6]-[#6:1].[#6:2]=[#6:3]-[#6:5]=[#8:8].[#8:7]   \n",
       "598     [#6]-[#6:1]-[#7:6]-[#6:5]=[#8:8].[#6:2].[#8:7]   \n",
       "599     [#6]-[#6:1]-[#7:6].[#6:2]=[#6:3].[#8:7].[#8:8]   \n",
       "\n",
       "               complement_smarts  mass_from_smi   abs_diff  \n",
       "0      [#6]-[#8:9].[#7:7].[#8:8]     139.063329  59.050239  \n",
       "1      [#6]-[#8:9].[#7:7].[#8:8]     139.063329  59.050239  \n",
       "2    [#6:6]-[#8:9].[#7:7].[#8:8]     139.063329  59.037664  \n",
       "3    [#6:4].[#6:5]:[#6:6]-[#8:9]     139.063329  59.037664  \n",
       "4      [#6].[#6:1].[#6:5]:[#6:6]     139.063329  59.037664  \n",
       "..                           ...            ...        ...  \n",
       "595                  [#6].[#7:6]     125.047678  27.024028  \n",
       "596                  [#6].[#7:6]     125.047678  27.024028  \n",
       "597                [#6:4]-[#7:6]     125.047678  26.987638  \n",
       "598                [#6:3].[#6:4]     125.047678  26.987638  \n",
       "599                [#6:4].[#6:5]     125.047678  26.987638  \n",
       "\n",
       "[105301 rows x 10 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop rows where abs(mz-common.mass_from_smi(row(smiles)) < 1)\n",
    "data['mass_from_smi'] = data['smiles'].apply(lambda x: mass_from_smi(x))\n",
    "data['abs_diff'] = abs(data['mz'] - data['mass_from_smi'])\n",
    "data_new = data[data['abs_diff'] > 1.1]\n",
    "\n",
    "# remove rows where smiles is CS(=O)(=O)O (this molecule causes issues in fragmentation because no fragmentation is predicted --> all SMARTS are empty)\n",
    "data_new = data_new[data_new['smiles'] != 'CS(=O)(=O)O']\n",
    "data_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions to extract the bond types, atom types and plot the heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_bond_type(bond_obj: Chem.Bond) -> str:\n",
    "    \"\"\"\n",
    "    Get the bond type as a string representation.\n",
    "    \n",
    "    Args:\n",
    "        bond_obj (Chem.Bond): The bond object from RDKit.\n",
    "    \n",
    "    Returns:\n",
    "        str: The bond type as a string.\n",
    "    \"\"\"\n",
    "    return {\n",
    "        Chem.BondType.SINGLE: '-',\n",
    "        Chem.BondType.DOUBLE: '=',\n",
    "        Chem.BondType.TRIPLE: '#',\n",
    "        Chem.BondType.AROMATIC: ':'\n",
    "    }.get(bond_obj.GetBondType(), '~')\n",
    "\n",
    "def get_neighbor_info(mol, atom_idx, exclude_idx) -> List[Tuple[str, str, int]]:\n",
    "    \"\"\"\n",
    "    Get information about neighboring atoms of a given atom in the molecule.\n",
    "\n",
    "    Args:\n",
    "        mol (Chem.Mol): The RDKit molecule object.\n",
    "        atom_idx (int): The index of the atom to analyze.\n",
    "        exclude_idx (int): The index of the atom to exclude from neighbors.\n",
    "\n",
    "    Returns:\n",
    "        List[Tuple[str, str, int]]: A list of tuples containing the symbol, bond type, and index of each neighbor.\n",
    "    \"\"\"\n",
    "    neighbors = []\n",
    "    for bond in mol.GetAtomWithIdx(atom_idx).GetBonds():\n",
    "        neigh = bond.GetOtherAtom(mol.GetAtomWithIdx(atom_idx))\n",
    "        if neigh.GetIdx() != exclude_idx and neigh.GetAtomicNum() > 1:\n",
    "            bond_type = get_bond_type(bond)\n",
    "            neighbors.append((neigh.GetSymbol(), bond_type, neigh.GetIdx()))\n",
    "    return sorted(neighbors, key=lambda x: x[2])\n",
    "\n",
    "def create_bond_string(mol, idx1, idx2, include_env: int = 0) -> str:\n",
    "    \"\"\"\n",
    "    Create a string representation of a bond between two atoms in a molecule.\n",
    "\n",
    "    Args:\n",
    "        mol (Chem.Mol): The RDKit molecule object.\n",
    "        idx1 (int): The index of the first atom.\n",
    "        idx2 (int): The index of the second atom.\n",
    "        include_env (int): Flag to include environment information \"degree/coordination shere\" (0, 1, or 2).\n",
    "\n",
    "    Returns:\n",
    "        str: The bond string representation.\n",
    "    \"\"\"\n",
    "    atom1, atom2 = mol.GetAtomWithIdx(idx1), mol.GetAtomWithIdx(idx2)\n",
    "    symbol1, symbol2 = atom1.GetSymbol(), atom2.GetSymbol()\n",
    "    bond_type = get_bond_type(mol.GetBondBetweenAtoms(idx1, idx2))\n",
    "    \n",
    "    if include_env == 2:\n",
    "        neighbors1 = get_neighbor_info(mol, idx1, idx2)\n",
    "        neighbors2 = get_neighbor_info(mol, idx2, idx1)\n",
    "        \n",
    "        env1 = ''.join(f\"({n[0]}{n[1]})\" for n in neighbors1)\n",
    "        env2 = ''.join(f\"({n[0]}{n[1]})\" for n in neighbors2)\n",
    "        \n",
    "        bond_string1 = f\"{env1}{symbol1}{bond_type}{symbol2}{env2}\"\n",
    "        bond_string2 = f\"{env2}{symbol2}{bond_type}{symbol1}{env1}\"\n",
    "        return min(bond_string1, bond_string2)\n",
    "    elif include_env == 1:\n",
    "        return f\"{min(symbol1, symbol2)}{bond_type}{max(symbol1, symbol2)}\"\n",
    "    elif include_env == 0:\n",
    "        return f\"{min(symbol1, symbol2)}~{max(symbol1, symbol2)}\"\n",
    "    else:\n",
    "        print(\"Invalid value for include_env. Must be 0, 1, or 2.\")\n",
    "\n",
    "def analyze_bonds(df: pd.DataFrame, include_environment: int = 0) -> Dict[str, Dict[str, float]]:\n",
    "    \"\"\"\n",
    "    Analyzes bonds purely based on fragment and complement SMARTS patterns.\n",
    "    \n",
    "    Args:\n",
    "        df (pd.DataFrame): DataFrame containing the data to analyze.\n",
    "        include_environment (int): Flag to include environment information \"degree/coordination shere\" (0, 1, or 2).\n",
    "\n",
    "    Returns:\n",
    "        Dict[str, Dict[str, float]]: A dictionary containing the analysis results.\n",
    "    \"\"\"\n",
    "    bond_break_counts = defaultdict(int)          \n",
    "    bond_intensities = defaultdict(float)         \n",
    "    bond_occurrences = defaultdict(int)     \n",
    "    total_break_intensity = 0                     \n",
    "    max_intensity = 0\n",
    "    \n",
    "    # First pass: count total occurrences of each bond type in structures using SMILES\n",
    "    processed_smiles = set()\n",
    "\n",
    "    \n",
    "    from tqdm import tqdm\n",
    "\n",
    "    for _, row in tqdm(df.iterrows(), total=len(df), desc='First pass'):\n",
    "        smiles = row.get('smiles', None)\n",
    "        if smiles and smiles not in processed_smiles:\n",
    "            mol = Chem.MolFromSmiles(smiles)\n",
    "            if mol is None:\n",
    "                continue\n",
    "                \n",
    "            processed_smiles.add(smiles)\n",
    "            \n",
    "            for bond in mol.GetBonds():\n",
    "                idx1, idx2 = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()\n",
    "                bond_string = create_bond_string(mol, idx1, idx2, include_env=include_environment)\n",
    "                bond_occurrences[bond_string] += 1\n",
    "\n",
    "    # Second pass: analyze fragmentations using fragment comparison\n",
    "    for _, row in tqdm(df.iterrows(), total=len(df), desc='Second pass'):\n",
    "        # Get the main molecule structure\n",
    "        mol = Chem.MolFromSmarts(row['smarts'])\n",
    "        if mol is None:\n",
    "            continue\n",
    "            \n",
    "        # Get fragment and complement molecules\n",
    "        fragment_mol = Chem.MolFromSmarts(row['fragment_smarts'])\n",
    "        try:\n",
    "            complement_mol = Chem.MolFromSmarts(row['complement_smarts'])\n",
    "        except:\n",
    "            complement_mol = None\n",
    "        \n",
    "        if fragment_mol is None or complement_mol is None:\n",
    "            continue\n",
    "            \n",
    "        # Get mapped atoms in each part\n",
    "        fragment_atoms = {atom.GetAtomMapNum() for atom in fragment_mol.GetAtoms() \n",
    "                        if atom.GetAtomMapNum() != 0}\n",
    "        complement_atoms = {atom.GetAtomMapNum() for atom in complement_mol.GetAtoms() \n",
    "                        if atom.GetAtomMapNum() != 0}\n",
    "        \n",
    "        # For each bond in the main molecule\n",
    "        for bond in mol.GetBonds():\n",
    "            begin_atom = bond.GetBeginAtom()\n",
    "            end_atom = bond.GetEndAtom()\n",
    "            begin_map = begin_atom.GetAtomMapNum()\n",
    "            end_map = end_atom.GetAtomMapNum()\n",
    "            \n",
    "            # Skip if either atom isn't mapped\n",
    "            if begin_map == 0 or end_map == 0:\n",
    "                continue\n",
    "                \n",
    "            # Check if this bond crosses between fragment and complement\n",
    "            if ((begin_map in fragment_atoms and end_map in complement_atoms) or\n",
    "                (end_map in fragment_atoms and begin_map in complement_atoms)):\n",
    "                \n",
    "                # This is a breaking bond!\n",
    "                bond_string = create_bond_string(mol, bond.GetBeginAtomIdx(), \n",
    "                                            bond.GetEndAtomIdx(), include_environment)\n",
    "                \n",
    "                bond_break_counts[bond_string] += 1\n",
    "                bond_intensities[bond_string] += row['intensity']\n",
    "                total_break_intensity += row['intensity']\n",
    "                if row['intensity'] > max_intensity:\n",
    "                    max_intensity = row['intensity']\n",
    "\n",
    "    total_structural_bonds = sum(bond_occurrences.values())\n",
    "    total_breaks = sum(bond_break_counts.values())\n",
    "    \n",
    "    normalized_intensities = {\n",
    "        bond: intensity / bond_occurrences.get(bond, 1)\n",
    "        for bond, intensity in bond_intensities.items()\n",
    "    }\n",
    "    \n",
    "    total_normalized_intensity = sum(normalized_intensities.values())\n",
    "    \n",
    "    # Which bonds are intrinsically most likely to break, regardless of how many times they appear in the structure?\n",
    "    # P-score = normalized intensity contribution per unique bond occurrence\n",
    "    preferential_score = {\n",
    "        bond: (norm_intensity / total_normalized_intensity) * 100\n",
    "        for bond, norm_intensity in normalized_intensities.items()\n",
    "    }\n",
    "\n",
    "    # In this implementation, we exclusively show the preferential score (pScore).\n",
    "    return {\n",
    "        'preferential_score': dict(preferential_score),\n",
    "        'bond_occurences': dict(bond_occurrences),\n",
    "    }\n",
    "\n",
    "def prepare_data(data, analyze_bonds_func, metrics_to_look_at):\n",
    "    \"\"\"\n",
    "    Prepares data for analysis by grouping it by collision energy and analyzing bonds.\n",
    "    \n",
    "    Args:\n",
    "        data (pd.DataFrame): The input data to analyze.\n",
    "        analyze_bonds_func (function): The function to analyze bonds.\n",
    "        metrics_to_look_at (list): List of metrics to include in the analysis.\n",
    "        \n",
    "    Returns:\n",
    "        Tuple[Dict[str, pd.DataFrame], List[float], List[str]]: A tuple containing:\n",
    "        - A dictionary of DataFrames for each metric.\n",
    "        - A list of unique collision energies.\n",
    "        - A list of unique bond types.\n",
    "        \n",
    "    \"\"\"\n",
    "    collision_energies = sorted(data['collision_energy'].unique())\n",
    "    \n",
    "    # First get all results for each collision energy\n",
    "    all_results = {ce: analyze_bonds_func(data[data['collision_energy'] == ce]) \n",
    "                  for ce in collision_energies}\n",
    "    \n",
    "    # Get ALL bond types from bond_occurences\n",
    "    all_bond_types = set()\n",
    "    for result in all_results.values():\n",
    "        all_bond_types.update(result['bond_occurences'].keys())\n",
    "        # Also include bond types from other metrics if needed\n",
    "        if 'bond_occurences' not in metrics_to_look_at:\n",
    "            for metric in metrics_to_look_at:\n",
    "                all_bond_types.update(result[metric].keys())\n",
    "    \n",
    "    # Create DataFrames\n",
    "    result_dfs = {}\n",
    "    for result_type in metrics_to_look_at:\n",
    "        # For other metrics, keep original behavior\n",
    "        df_data = {ce: {bt: 0.0 for bt in all_bond_types} \n",
    "                    for ce in collision_energies}\n",
    "        \n",
    "        for ce, result in all_results.items():\n",
    "            for bt, value in result[result_type].items():\n",
    "                df_data[ce][bt] = value\n",
    "        \n",
    "        result_dfs[result_type] = pd.DataFrame(df_data).T\n",
    "    \n",
    "    return result_dfs, collision_energies, list(sorted(all_bond_types))\n",
    "\n",
    "\n",
    "def plot_3d_by_bond(grouped_data, collision_energies, bond_types, result_type,\n",
    "                    cmap='viridis', figsize=(12,8)):\n",
    "    # ---- prepare display_data as before ----\n",
    "    display_data = (grouped_data[bond_types]\n",
    "                    .sort_index()\n",
    "                    .fillna(0))\n",
    "\n",
    "    display_data = display_data.div(display_data.sum(axis=1), axis=0) * 100\n",
    "\n",
    "    # numeric arrays\n",
    "    #Y = np.array(collision_energies, dtype=float)    # now the vertical axis\n",
    "    Y = np.arange(len(collision_energies))\n",
    "    n_e, n_b = display_data.shape                    # (#energies, #bonds)\n",
    "\n",
    "    # X axis will be just integer indices for each bond type\n",
    "    X_idx = np.arange(n_b)\n",
    "\n",
    "    fig = plt.figure(figsize=figsize, dpi=400)\n",
    "    ax  = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "    # set up your axes ticks & labels\n",
    "    ax.set_xticks(X_idx)\n",
    "    ax.set_xticklabels(display_data.columns)\n",
    "    ax.set_yticks(Y)\n",
    "    ax.set_yticklabels(collision_energies)\n",
    "    ax.set_xlabel('Bond type')\n",
    "    ax.set_ylabel('Collision energy (eV)')\n",
    "    ax.set_zlabel('Average contribution\\nto intensity (%)')\n",
    "    ax.xaxis.labelpad=-3\n",
    "    ax.yaxis.labelpad=-3\n",
    "    ax.zaxis.labelpad=-3\n",
    "\n",
    "    # color normalization over bond types\n",
    "    norm      = plt.Normalize(0, n_b - 1)\n",
    "    cmap_inst = plt.get_cmap(cmap)\n",
    "\n",
    "    # for each bond type, build and draw its \"curtain\" + top edge\n",
    "    for i, bond in enumerate(display_data.columns):\n",
    "        zs = display_data[bond].values\n",
    "        xs = np.full_like(Y, i, dtype=float)\n",
    "\n",
    "        # build the “curtain” polygon\n",
    "        top    = np.column_stack((xs,       Y,       zs))\n",
    "        bottom = np.column_stack((xs[::-1], Y[::-1], np.zeros_like(zs)))\n",
    "        verts  = np.vstack((top, bottom))\n",
    "\n",
    "        poly = Poly3DCollection([verts], alpha=0.2)\n",
    "        color = cmap_inst(norm(i))\n",
    "        poly.set_facecolor(color)\n",
    "        poly.set_edgecolor('none')\n",
    "        ax.add_collection3d(poly)\n",
    "\n",
    "        # draw its 3D line on top\n",
    "        ax.plot(xs, Y, zs, color=color, linewidth=0.8)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    fig.savefig('preferential.pdf', bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot bond-breaking frequency (Ext Data Fig 3)\n",
    "\n",
    "Give scores for environments such as C-C, C:C, C=O, ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "First pass: 100%|██████████████████████████████████████████████████████████████| 17900/17900 [00:00<00:00, 23063.65it/s]\n",
      "Second pass: 100%|██████████████████████████████████████████████████████████████| 17900/17900 [00:14<00:00, 1254.92it/s]\n",
      "First pass: 100%|██████████████████████████████████████████████████████████████| 17900/17900 [00:00<00:00, 22401.00it/s]\n",
      "Second pass: 100%|██████████████████████████████████████████████████████████████| 17900/17900 [00:14<00:00, 1273.99it/s]\n",
      "First pass: 100%|██████████████████████████████████████████████████████████████| 17900/17900 [00:00<00:00, 22563.46it/s]\n",
      "Second pass: 100%|██████████████████████████████████████████████████████████████| 17900/17900 [00:13<00:00, 1284.09it/s]\n",
      "First pass: 100%|██████████████████████████████████████████████████████████████| 17900/17900 [00:00<00:00, 22068.29it/s]\n",
      "Second pass: 100%|██████████████████████████████████████████████████████████████| 17900/17900 [00:14<00:00, 1273.66it/s]\n",
      "First pass: 100%|██████████████████████████████████████████████████████████████| 17900/17900 [00:00<00:00, 21970.99it/s]\n",
      "Second pass: 100%|██████████████████████████████████████████████████████████████| 17900/17900 [00:14<00:00, 1245.60it/s]\n",
      "First pass: 100%|██████████████████████████████████████████████████████████████| 17900/17900 [00:00<00:00, 22641.27it/s]\n",
      "Second pass: 100%|██████████████████████████████████████████████████████████████| 17900/17900 [00:14<00:00, 1262.67it/s]\n"
     ]
    }
   ],
   "source": [
    "# 3D plotting without environment\n",
    "data_frames_1, collision_energies_1, bond_types_1 = prepare_data(data, lambda df: analyze_bonds(df, include_environment=1), metrics_to_look_at=['preferential_score', 'bond_occurences'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3856309/83397537.py:282: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_3d_by_bond(data_frames_1['preferential_score'], collision_energies_1, ['C:N', 'C:C', 'C-O', 'C=O', 'C-N', 'C-C'], 'preferential_score', cmap='viridis', \n",
    "                figsize=(2,2))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (ms-main)",
   "language": "python",
   "name": "ms-main"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
